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General Information
Dr. Lin Huang
Metropolitan State University of Denver, USA
It's my honor to take on the position of editor in chief of IJMLC. We encourage authors to submit papers concerning any branch of machine learning and computing.
IJMLC 2015 Vol. 5(3): 187-191 ISSN: 2010-3700
DOI: 10.7763/IJMLC.2015.V5.505

Pruning Classification Rules with Instance Reduction Methods

Osama M. Othman and Christopher H. Bryant
Abstract—Generating classification rules from data often leads to large sets of rules that need to be pruned. A new pre-pruning technique for rule induction is presented which applies instance reduction before rule induction. Training three rule classifiers on datasets that have been reduced earlier with instance reduction methods leads to a statistically significant lower number of generated rules, without adversely affecting the predictive performance. The search strategies used by the three algorithms vary in terms of both type (depth-first or beam search) and direction (general-to-specific or specific-to-general).

Index Terms—Rule induction, noise filtering, instance reduction.

O. M. Othman and C. H. Bryant are with the School of Computing, Science and Engineering Newton Building, the University of Salford, Greater Manchester, M5,4WT, England, UK (e-mail:O.Othman@edu.salford.ac.uk, C.H.Bryant@salford.ac.uk).


Cite: Osama M. Othman and Christopher H. Bryant, "Pruning Classification Rules with Instance Reduction Methods," International Journal of Machine Learning and Computing vol. 5, no. 3, pp. 187-191, 2015.

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